Development and Validation of a Natural Language Processing Tool to Enable Clinical Research in Emergency and Acute Care Medicine: Retrospective Cohort Study

BACKGROUND AND RATIONALE FOR THE STUDY

Conducting clinical and quality-of-care assessment research in emergency medicine is as difficult as it is important. It is difficult because the vast number of patients that need to be treated and the chronic shortage of staff make ad hoc data collection impractical. It is important because, in the end, research enables emergency physicians and nurses to base their practice on evidence obtained in their own, unique setting, as opposed to evidence obtained in far-removed contexts, as is commonly the case today.

The only way to bridge the gap between research needs and availability of robust data is to extract data directly from the electronic health records (EHRs) of emergency departments, avoiding dedicated, time-consuming data collection. This is a difficult task, however, because the most useful information is in free text format (e.g., presence of signs and symptoms, suspected and confirmed diagnosis, anamnesis). Such circumstances and needs require a reliable natural language processing (NLP) tool to derive highly consistent data from free text.

Today, large-scale language models are available that can accurately interpret natural language. These models are trained on huge amounts of general knowledge taken mostly from the Internet, however, so their performance in more specialized areas, such as the medical domain, may not be optimal.

The present study is part of a larger project called eCREAM (enabling Clinical Research in Emergency and Acute-care Medicine), and aims to develop and validate a language model (called eCREAM_LM) for six languages that can interpret the contents of emergency department EHRs and extract relevant information for research purposes.